






'''simple docstring'''



import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
      import numpy as np
      import torch

      from . import (
          AlbertForPreTraining,
          BartForConditionalGeneration,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          CamembertForMaskedLM,
          CTRLLMHeadModel,
          DistilBertForMaskedLM,
          DistilBertForQuestionAnswering,
          DPRContextEncoder,
          DPRQuestionEncoder,
          DPRReader,
          ElectraForPreTraining,
          FlaubertWithLMHeadModel,
          GPTaLMHeadModel,
          LayoutLMForMaskedLM,
          LxmertForPreTraining,
          LxmertVisualFeatureEncoder,
          OpenAIGPTLMHeadModel,
          RobertaForMaskedLM,
          RobertaForSequenceClassification,
          TaForConditionalGeneration,
          TransfoXLLMHeadModel,
          XLMRobertaForMaskedLM,
          XLMWithLMHeadModel,
          XLNetLMHeadModel,
      )


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE							=						{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							str       ,   __A			:							List[Any]       ,   __A			:							int       ,   __A			:							Dict=False       ,   __A			:							List[Any]=True      )							->   int:



 """simple docstring"""




 if model_type not in MODEL_CLASSES:
  raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()      )}.'      )

 lowercase	,		lowercase	,		lowercase	,		lowercase       : str						=MODEL_CLASSES[model_type]

 # Initialise TF model
 if config_file in aws_config_map:
  lowercase       : Tuple						=cached_file(__A       ,   __A       ,   force_download=not use_cached_models      )
 lowercase       : Dict						=config_class.from_json_file(__A      )
 lowercase       : Any						=True
 lowercase       : str						=True
 print(F'Building TensorFlow model from configuration: {config}'      )
 lowercase       : Union[str, Any]						=model_class(__A      )

 # Load weights from tf checkpoint
 if pytorch_checkpoint_path in aws_config_map.keys():
  lowercase       : List[str]						=cached_file(
      __A       ,   __A       ,   force_download=not use_cached_models      )
 # Load PyTorch checkpoint in tf2 model:
 lowercase       : List[str]						=load_pytorch_checkpoint_in_tfa_model(__A       ,   __A      )

 if compare_with_pt_model:
  lowercase       : Dict						=tf_model(tf_model.dummy_inputs       ,   training=__A      )  # build the network

  lowercase       : Optional[int]						=torch.load(__A       ,   map_location='''cpu'''      )
  lowercase       : Optional[Any]						=pt_model_class.from_pretrained(
      pretrained_model_name_or_path=__A       ,   config=__A       ,   state_dict=__A      )

  with torch.no_grad():
   lowercase       : Tuple						=pt_model(**pt_model.dummy_inputs      )

  lowercase       : Any						=pto[0].numpy()
  lowercase       : List[str]						=tfo[0].numpy()
  lowercase       : int						=np.amax(np.abs(np_pt - np_tf      )      )
  print(F'Max absolute difference between models outputs {diff}'      )
  assert diff <= 2E-2, F'Error, model absolute difference is >2e-2: {diff}'

 # Save pytorch-model
 print(F'Save TensorFlow model to {tf_dump_path}'      )
 tf_model.save_weights(__A       ,   save_format='''h5'''      )


def 				lowercase_					(     __A			:							List[str]       ,   __A			:							str       ,   __A			:							List[Any]=None       ,   __A			:							str=None       ,   __A			:							str=False       ,   __A			:							str=False       ,   __A			:							Union[str, Any]=False       ,   __A			:							Dict=False       ,   )							->   List[Any]:



 """simple docstring"""




 if args_model_type is None:
  lowercase       : int						=list(MODEL_CLASSES.keys()      )
 else:
  lowercase       : List[Any]						=[args_model_type]

 for j, model_type in enumerate(__A       ,   start=1      ):
  print('''=''' * 1_0_0      )
  print(F' Converting model type {j}/{len(__A      )}: {model_type}'      )
  print('''=''' * 1_0_0      )
  if model_type not in MODEL_CLASSES:
   raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()      )}.'      )

  lowercase	,		lowercase	,		lowercase	,		lowercase	,		lowercase       : Any						=MODEL_CLASSES[model_type]

  if model_shortcut_names_or_path is None:
   lowercase       : str						=list(aws_model_maps.keys()      )
  if config_shortcut_names_or_path is None:
   lowercase       : Union[str, Any]						=model_shortcut_names_or_path

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(__A       ,   __A      )       ,   start=1      ):
   print('''-''' * 1_0_0      )
   if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
    if not only_convert_finetuned_models:
     print(F'    Skipping finetuned checkpoint {model_shortcut_name}'      )
     continue
    lowercase       : Optional[Any]						=model_shortcut_name
   elif only_convert_finetuned_models:
    print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'      )
    continue
   print(
       F'    Converting checkpoint {i}/{len(__A      )}: {model_shortcut_name} - model_type {model_type}'      )
   print('''-''' * 1_0_0      )

   if config_shortcut_name in aws_config_map:
    lowercase       : int						=cached_file(__A       ,   __A       ,   force_download=not use_cached_models      )
   else:
    lowercase       : List[str]						=config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    lowercase       : int						=cached_file(__A       ,   __A       ,   force_download=not use_cached_models      )
   else:
    lowercase       : Optional[int]						=model_shortcut_name

   if os.path.isfile(__A      ):
    lowercase       : str						='''converted_model'''

   convert_pt_checkpoint_to_tf(
       model_type=__A       ,   pytorch_checkpoint_path=__A       ,   config_file=__A       ,   tf_dump_path=os.path.join(__A       ,   model_shortcut_name + '''-tf_model.h5'''      )       ,   compare_with_pt_model=__A       ,   )
   if remove_cached_files:
    os.remove(__A      )
    os.remove(__A      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
      )
      parser.add_argument(
          '--model_type',
          default=None,
          type=str,
          help=(
              f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
              'convert all the models from AWS.'
          ),
      )
      parser.add_argument(
          '--pytorch_checkpoint_path',
          default=None,
          type=str,
          help=(
              'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
              'If not given, will download and convert all the checkpoints from AWS.'
          ),
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          help=(
              'The config json file corresponding to the pre-trained model. \n'
              'This specifies the model architecture. If not given and '
              '--pytorch_checkpoint_path is not given or is a shortcut name '
              'use the configuration associated to the shortcut name on the AWS'
          ),
      )
      parser.add_argument(
          '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
      )
      parser.add_argument(
          '--use_cached_models',
          action='store_true',
          help='Use cached models if possible instead of updating to latest checkpoint versions.',
      )
      parser.add_argument(
          '--remove_cached_files',
          action='store_true',
          help='Remove pytorch models after conversion (save memory when converting in batches).',
      )
      parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      # if args.pytorch_checkpoint_path is not None:
      #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
      #                                 args.pytorch_checkpoint_path,
      #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
      #                                 args.tf_dump_path,
      #                                 compare_with_pt_model=args.compare_with_pt_model,
      #                                 use_cached_models=args.use_cached_models)
      # else:
      convert_all_pt_checkpoints_to_tf(
          args.model_type.lower() if args.model_type is not None else None,
          args.tf_dump_path,
          model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
          if args.pytorch_checkpoint_path is not None
          else None,
          config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
          compare_with_pt_model=args.compare_with_pt_model,
          use_cached_models=args.use_cached_models,
          remove_cached_files=args.remove_cached_files,
          only_convert_finetuned_models=args.only_convert_finetuned_models,
      )




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase					={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["MobileBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileBertForMaskedLM",
      "MobileBertForMultipleChoice",
      "MobileBertForNextSentencePrediction",
      "MobileBertForPreTraining",
      "MobileBertForQuestionAnswering",
      "MobileBertForSequenceClassification",
      "MobileBertForTokenClassification",
      "MobileBertLayer",
      "MobileBertModel",
      "MobileBertPreTrainedModel",
      "load_tf_weights_in_mobilebert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileBertForMaskedLM",
      "TFMobileBertForMultipleChoice",
      "TFMobileBertForNextSentencePrediction",
      "TFMobileBertForPreTraining",
      "TFMobileBertForQuestionAnswering",
      "TFMobileBertForSequenceClassification",
      "TFMobileBertForTokenClassification",
      "TFMobileBertMainLayer",
      "TFMobileBertModel",
      "TFMobileBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
